Aircraft radar target detection method based on GCV (generalized cross validation)

Abstract

The present invention discloses an aircraft radar target detection method based on the GCV, comprising the following steps: (1) converting a detection problem of the aircraft radar to the target to a constraint optimization problem of solution of a diagonal loading parameter; (2) converting the constraint optimization problem of solution of the diagonal loading parameter to a penalty function coefficient estimation problem of a Tikhonov project, and constructing a constraint optimization problem of solution of the penalty function coefficient on the basis of the generalized cross validation criterion; (3) performing singular value decomposition of a coefficient matrix in the constraint optimization problem of solution of the penalty function coefficient, obtaining a simplified objective function according to the expansion mode of the singular value of the coefficient matrix, and obtaining a final diagonal loading parameter by utilizing the secant method; and (4) calculating a filtering output value of a detection unit according to the final diagonal loading parameter, comparing the filtering output value of the detection unit with a default threshold value, and determining whether the detection unit has a target signal or not.

Description

technical field [0001] The invention belongs to the field of radar technology, and relates to a GCV-based airborne radar target detection method, which is used to solve the problem of calculating diagonal loading parameters in airborne radar based on diagonal loading estimation covariance matrix estimation, and can be used to improve space-time Adaptive processing performance. Background technique [0002] Space Time Adaptive Processing (STAP) is a filtering method combining airspace and time domain, which can effectively suppress ground clutter and improve the detection ability of airborne radar for moving targets. When STAP calculates the adaptive filtering weight vector, it needs to use the expected covariance matrix of the clutter distribution and the noise distribution. In the actual situation, the expected covariance matrix cannot be obtained. At this time, STAP usually uses the data of the distance dimension as the training sample to estimate Covariance matrix; when ...

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Problems solved by technology

GuerciJR, ZhuC and others proposed a rank reduction method, which is a method based on the characteristic subspace, which utilizes the low-rank characteristics of the space-time covariance matrix. However, the determination of the clutter rank in this method is a relatively Complicated problems; due to the leakage of clutter subspace, the theoretically calculated clutter rank is inconsistent with the actual clutter rank, resulting in a decrease in the performance of the rank reduction method

YangZC, Ma Zeqiang et al. proposed a direct data domain method based on sparse recovery. This method utilizes the sparsity of the space-time snapshot data in the angular Doppler domain, and uses the sparse recovery method to obtain the space-time two-dimensional spectrum of the clutter. Then The covariance matrix of the clutter is reconstructed by using the space-time two-dimensional spectrum of the clutter and the dictionary matrix. However, the array element error and channel error will cause the constructed dictionary matrix to not match the actual data, resulting in sparsely restored clutter The inaccuracy of the space-time two-dimensional spectrum of and the error of the reconstructed covariance matrix

CarlsonBD proposed a covariance matrix estimation method based on diagonal loading. This method improves the estimation accuracy of the covariance matrix by fusing the sampling covariance matrix and the structured diagonal matrix. This method has low computational complexity, strong practicability, and robustness. Obvious gains have been made in beamforming and moving target detection. Among them, the diagonal loading parameters in the structured diagonal matrix can usually be determined according to the noise power level of the airborne radar system. However, in practical engineering applications, the noise Real-time and accurate measurement of power is very difficult

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